Redefining plant systems biology: from cell to ecosystem

How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer's Disease Research and Clinical Practices?

The accumulation of amyloid-β (Aβ) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aβ, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aβ levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aβ (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aβ, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.

Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale

Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions.

Mathematical Modeling Reveals That Sucrose Regulates Leaf Senescence via Dynamic Sugar Signaling Pathways

Sucrose (Suc) accumulation is one of the key indicators of leaf senescence onset, but little is known about its regulatory role. Here, we found that application of high (120–150 mM) and low levels (60 mM) of Suc to young leaf (YL) and fully expanded leaf (FEL) discs, respectively, decreased chlorophyll content and maximum photosynthetic efficiency. Electrolyte leakage and malondialdehyde levels increased at high Suc concentrations (90–120 mM in YL and 60 and 150 mM in FEL discs). In FEL discs, the senescence-associated gene NtSAG12 showed a gradual increase in expression with increased Suc application; in contrast, in YL discs, NtSAG12 was upregulated with low Suc treatment (60 mM) but downregulated at higher levels of Suc. In YL discs, trehalose-6-phosphate (T6P) accumulated at a low half-maximal effective concentration (EC50) of Suc (1.765 mM). However, T6P levels declined as trehalose 6 phosphate synthase (TPS) content decreased, resulting in the maximum velocity of sucrose non-fermenting-1-related protein kinase (SnRK) and hexokinase (HXK) occurring at higher level of Suc. We therefore speculated that senescence was induced by hexose accumulation. In FEL discs, the EC50 of T6P occurred at a low concentration of Suc (0.9488 mM); T6P levels progressively increased with higher TPS content, which inhibited SnRK activity with a dissociation constant (K_d) of 0.001475 U/g. This confirmed that the T6P–SnRK complex induced senescence in detached FEL discs.

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

Dual herbivore attack and herbivore density affect metabolic profiles of Brassica nigra leaves

Plant responses to dual herbivore attack are increasingly studied, but effects on the metabolome have largely been restricted to volatile metabolites and defence-related non-volatile metabolites. However, plants subjected to stress, such as herbivory, undergo major changes in both primary and secondary metabolism. Using a naturally occurring system, we investigated metabolome-wide effects of single or dual herbivory on Brassica nigra plants by Brevicoryne brassicae aphids and Pieris brassicae caterpillars, while also considering the effect of aphid density. Metabolomic analysis of leaf material showed that single and dual herbivory had strong effects on the plant metabolome, with caterpillar feeding having the strongest influence. Additionally, aphid-density-dependent effects were found in both the single and dual infestation scenarios. Multivariate analysis revealed treatment-specific metabolomic profiles, and effects were largely driven by alterations in the glucosinolate and sugar pools. Our work shows that analysing the plant metabolome as a single entity rather than as individual metabolites provides new insights into the subcellular processes underlying plant defence against multiple herbivore attackers. These processes appear to be importantly influenced by insect density.

AraQTL - workbench and archive for systems genetics in Arabidopsis thaliana

Genetical genomics studies uncover genome-wide genetic interactions between genes and their transcriptional regulators. High-throughput measurement of gene expression in recombinant inbred line populations has enabled investigation of the genetic architecture of variation in gene expression. This has the potential to enrich our understanding of the molecular mechanisms affected by and underlying natural variation. Moreover, it contributes to the systems biology of natural variation, as a substantial number of experiments have resulted in a valuable amount of interconnectable phenotypic, molecular and genotypic data. A number of genetical genomics studies have been published for Arabidopsis thaliana, uncovering many expression quantitative trait loci (eQTLs). However, these complex data are not easily accessible to the plant research community, leaving most of the valuable genetic interactions unexplored as cross-analysis of these studies is a major effort. We address this problem with AraQTL (http://www.bioinformatics.nl/Ara QTL/), an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local eQTLs that could harbour candidate genes for phenotypic QTLs, or detect gene-by-environment interactions by comparing eQTLs under different conditions.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes.

Jasmonate signalling in plants shapes plant-insect interaction ecology

The phytohormone jasmonic acid (JA) regulates the induction of direct and indirect defences against herbivores. By now, the biochemical pathway of JA-signalling has been well resolved, allowing the use of an interdisciplinary toolbox and spurring the mechanistic investigation of plant-insect interactions. Recent advances show that JA-mediated plant responses are involved in the competitive and trophic interactions between various organisms throughout at least four trophic levels and therefore likely shape natural communities. Moreover, JA-mediated responses can be primed or suppressed by various environmental factors that are related to herbivory or not. Yet, to integrate the complex interactions at the physiological and ecological levels into community ecology, an examination of the often onetime discoveries for general rules and new bioinformatic approaches are required.

Piriformospora indica: Potential and Significance in Plant Stress Tolerance

Owing to its exceptional ability to efficiently promote plant growth, protection and stress tolerance, a mycorrhiza like endophytic Agaricomycetes fungus Piriformospora indica has received a great attention over the last few decades. P. indica is an axenically cultiviable fungus which exhibits its versatility for colonizing/hosting a broad range of plant species through directly manipulating plant hormone-signaling pathway during the course of mutualism. P. indica-root colonization leads to a better plant performance in all respect, including enhanced root proliferation by indole-3-acetic acid production which in turn results into better nutrient-acquisition and subsequently to improved crop growth and productivity. Additionally, P. indica can induce both local and systemic resistance to fungal and viral plant diseases through signal transduction. P. indica-mediated stimulation in antioxidant defense system components and expressing stress-related genes can confer crop/plant stress tolerance. Therefore, P. indica can biotize micropropagated plantlets and also help these plants to overcome transplantation shock. Nevertheless, it can also be involved in a more complex symbiotic relationship, such as tripartite symbiosis and can enhance population dynamic of plant growth promoting rhizobacteria. In brief, P. indica can be utilized as a plant promoter, bio-fertilizer, bioprotector, bioregulator, and biotization agent. The outcome of the recent literature appraised herein will help us to understand the physiological and molecular bases of mechanisms underlying P. indica-crop plant mutual relationship. Together, the discussion will be functional to comprehend the usefulness of crop plant-P. indica association in both achieving new insights into crop protection/improvement as well as in sustainable agriculture production.

Beyond the Cell: Using Multiscalar Topics to Bring Interdisciplinarity into Undergraduate Cellular Biology Courses

Western science has grown increasingly reductionistic and, in parallel, the undergraduate life sciences curriculum has become disciplinarily fragmented. While reductionistic approaches have led to landmark discoveries, many of the most exciting scientific advances in the late 20th century have occurred at disciplinary interfaces; work at these interfaces is necessary to manage the world's looming problems, particularly those that are rooted in cellular-level processes but have ecosystem- and even global-scale ramifications (e.g., nonsustainable agriculture, emerging infectious diseases). Managing such problems requires comprehending whole scenarios and their emergent properties as sums of their multiple facets and complex interrelationships, which usually integrate several disciplines across multiple scales (e.g., time, organization, space). This essay discusses bringing interdisciplinarity into undergraduate cellular biology courses through the use of multiscalar topics. Discussing how cellular-level processes impact large-scale phenomena makes them relevant to everyday life and unites diverse disciplines (e.g., sociology, cell biology, physics) as facets of a single system or problem, emphasizing their connections to core concepts in biology. I provide specific examples of multiscalar topics and discuss preliminary evidence that using such topics may increase students' understanding of the cell's position within an ecosystem and how cellular biology interfaces with other disciplines.

Alanine aminotransferase variants conferring diverse NUE phenotypes in Arabidopsis thaliana

Alanine aminotransferase (AlaAT, E.C. 2.6.1.2), is a pyridoxal-5'-phosphate-dependent (PLP) enzyme that catalyzes the reversible transfer of an amino group from alanine to 2-oxoglutarate to produce glutamate and pyruvate, or vice versa. It has been well documented in both greenhouse and field studies that tissue-specific over-expression of AlaAT from barley (Hordeum vulgare, HvAlaAT) results in a significant increase in plant NUE in both canola and rice. While the physical phenotypes associated with over-expression of HvAlaAT have been well characterized, the role this enzyme plays in vivo to create a more N efficient plant remains unknown. Furthermore, the importance of HvAlaAT, in contrast to other AlaAT enzyme homologues in creating this phenotype has not yet been explored. To address the role of AlaAT in NUE, AlaAT variants from diverse sources and different subcellular locations, were expressed in the wild-type Arabidopsis thaliana Col-0 background and alaat1;2 (alaat1-1;alaat2-1) knockout background in various N environments. The analysis and comparison of both the physical and physiological properties of AlaAT over-expressing transgenic plants demonstrated significant differences between plants expressing the different AlaAT enzymes under different external conditions. This analysis indicates that the over-expression of AlaAT variants other than HvAlaAT in crop plants could further increase the NUE phenotype(s) previously observed.

Multidimensional approaches for studying plant defence against insects: from ecology to omics and synthetic biology

The biggest challenge for modern biology is to integrate multidisciplinary approaches towards understanding the organizational and functional complexity of biological systems at different hierarchies, starting from the subcellular molecular mechanisms (microscopic) to the functional interactions of ecological communities (macroscopic). The plant-insect interaction is a good model for this purpose with the availability of an enormous amount of information at the molecular and the ecosystem levels. Changing global climatic conditions are abruptly resetting plant-insect interactions. Integration of discretely located heterogeneous information from the ecosystem to genes and pathways will be an advantage to understand the complexity of plant-insect interactions. This review will present the recent developments in omics-based high-throughput experimental approaches, with particular emphasis on studying plant defence responses against insect attack. The review highlights the importance of using integrative systems approaches to study plant-insect interactions from the macroscopic to the microscopic level. We analyse the current efforts in generating, integrating and modelling multiomics data to understand plant-insect interaction at a systems level. As a future prospect, we highlight the growing interest in utilizing the synthetic biology platform for engineering insect-resistant plants.

Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature?

A crop model with genetic inputs can potentially simulate yield for a large range of genotypes, sites, and years, thereby indicating where and when a given combination of alleles confers a positive effect. We discuss to what extent current crop models, developed for predicting the effects of climate or cultivation techniques on a reference genotype, are adequate for ranking yields of a large number of genotypes in climatic scenarios with water deficit or high temperatures. We compare here the algorithms involved in 19 crop models. Marked differences exist in the representation of the combined effects of temperature and water deficit on plant development, and in the coordination of these effects with biomass production. The current literature suggests that these differences have a small impact on the yield prediction of a reference genotype because errors on the effects of different traits compensate each other. We propose that they have a larger impact if the crop model is used in a genetic context, because the model has to account for the genetic variability of studied traits. Models with explicit genetic inputs will be increasingly feasible because model parameters corresponding to each genotype can now be measured in phenotyping platforms for large plant collections. This will in turn allow prediction of parameter values from the allelic composition of genotypes. It is therefore timely to adapt crop models to this new context to simulate the allelic effects in present or future climatic scenarios with water or heat stresses.

Plant systems biology: insights, advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

A DNA-binding-site landscape and regulatory network analysis for NAC transcription factors in Arabidopsis thaliana

Target gene identification for transcription factors is a prerequisite for the systems wide understanding of organismal behaviour. NAM-ATAF1/2-CUC2 (NAC) transcription factors are amongst the largest transcription factor families in plants, yet limited data exist from unbiased approaches to resolve the DNA-binding preferences of individual members. Here, we present a TF-target gene identification workflow based on the integration of novel protein binding microarray data with gene expression and multi-species promoter sequence conservation to identify the DNA-binding specificities and the gene regulatory networks of 12 NAC transcription factors. Our data offer specific single-base resolution fingerprints for most TFs studied and indicate that NAC DNA-binding specificities might be predicted from their DNA-binding domain's sequence. The developed methodology, including the application of complementary functional genomics filters, makes it possible to translate, for each TF, protein binding microarray data into a set of high-quality target genes. With this approach, we confirm NAC target genes reported from independent in vivo analyses. We emphasize that candidate target gene sets together with the workflow associated with functional modules offer a strong resource to unravel the regulatory potential of NAC genes and that this workflow could be used to study other families of transcription factors.

A transcriptomics-based kinetic model for ethylene biosynthesis in tomato (Solanum lycopersicum) fruit: development, validation and exploration of novel regulatory mechanisms

The gaseous plant hormone ethylene is involved in many physiological processes including climacteric fruit ripening, in which it is a key determinant of fruit quality. A detailed model that describes ethylene biochemistry dynamics is missing. Often, kinetic modeling is used to describe metabolic networks or signaling cascades, mostly ignoring the link with transcriptomic data. We have constructed an elegant kinetic model that describes the transfer of genetic information into abundance and metabolic activity of proteins for the entire ethylene biosynthesis pathway during fruit development and ripening of tomato (Solanum lycopersicum). Our model was calibrated against a vast amount of transcriptomic, proteomic and metabolic data and showed good descriptive qualities. Subsequently it was validated successfully against several ripening mutants previously described in the literature. The model was used as a predictive tool to evaluate novel and existing hypotheses regarding the regulation of ethylene biosynthesis. This bottom-up kinetic network model was used to indicate that a side-branch of the ethylene pathway, the formation of the dead-end product 1-(malonylamino)-1-aminocyclopropane-1-carboxylic acid (MACC), might have a strong effect on eventual ethylene production. Furthermore, our in silico analyses indicated potential (post-) translational regulation of the ethylene-forming enzyme ACC oxidase.

Why is it so difficult to identify a single indicator of water stress in plants? A proposal for a multivariate analysis to assess emergent properties

Because of the complexity of plant responses to water deficit, researchers have attempted to identify simplified models to understand critical aspects of the problem by searching for single indicators that would enable evaluations of the effects of environmental changes on the entire plant. However, this reductionist approach, which is often used in plant sciences, makes it difficult to distinguish systemic emergent behaviours. Currently, a new class of models and epistemology have called attention to the fundamental properties of complex systems. These properties, termed 'emergent', are observed at a large scale of the system (top hierarchical level) but cannot be observed or inferred from smaller scales of observation in the same system. We propose that multivariate statistical analysis can provide a suitable tool to quantify global responses to water deficit, allowing a specific and partially quantitative assessment of emergent properties. Based on an experimental study, our results showed that the classical approach of the individual analysis of different data sets might provide different interpretations for the observed effects of water deficit. These results support the hypothesis that a cross-scale multivariate analysis is an appropriate method to establish models for systemic understanding of the interactions between plants and their changing environment.

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology

BACKGROUND

Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology.

METHODOLOGY AND PRINCIPAL FINDINGS

In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1).

CONCLUSIONS

Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

Field-omics-understanding large-scale molecular data from field crops

The recent advances in gene expression analysis as well as protein and metabolite quantification enable genome-scale capturing of complex biological processes at the molecular level in crop field trials. This opens up new possibilities for understanding the molecular and environmental complexity of field-based systems and thus shedding light on the black box between genotype and environment, which in agriculture always is influenced by a multi-stress environment and includes management interventions. Nevertheless, combining different types of data obtained from the field and making biological sense out of large datasets remain challenging. Here we highlight the need to create a cross-disciplinary platform for innovative experimental design, sampling and subsequent analysis of large-scale molecular data obtained in field trials. For these reasons we put forward the term field-omics: "Field-omics strives to couple information from genomes, transcriptomes, proteomes, metabolomes and metagenomes to the long-established practice in crop science of conducting field trials as well as to adapt current strategies for recording and analysing field data to facilitate integration with '-omics' data."

Getting to the edge: protein dynamical networks as a new frontier in plant-microbe interactions

A systems perspective on diverse phenotypes, mechanisms of infection, and responses to environmental stresses can lead to considerable advances in agriculture and medicine. A significant promise of systems biology within plants is the development of disease-resistant crop varieties, which would maximize yield output for food, clothing, building materials, and biofuel production. A systems or "-omics" perspective frames the next frontier in the search for enhanced knowledge of plant network biology. The functional understanding of network structure and dynamics is vital to expanding our knowledge of how the intercellular communication processes are executed. This review article will systematically discuss various levels of organization of systems biology beginning with the building blocks termed "-omes" and ending with complex transcriptional and protein-protein interaction networks. We will also highlight the prevailing computational modeling approaches of biological regulatory network dynamics. The latest developments in the "-omics" approach will be reviewed and discussed to underline and highlight novel technologies and research directions in plant network biology.

Crop management impacts the efficiency of quantitative trait loci (QTL) detection and use: case study of fruit load×QTL interactions

Mapping studies using populations with introgressed marker-defined genomic regions are continuously increasing knowledge about quantitative trait loci (QTL) that correlate with variation in important crop traits. This knowledge is useful for plant breeding, although combining desired traits in one genotype might be complicated by the mode of inheritance and co-localization of QTL with antagonistic effects, and by physiological trade-offs, and feed-back or feed-forward mechanisms. Therefore, integrating advances at the genetic level with insight into influences of environment and crop management on crop performance remains difficult. Whereas mapping studies can pinpoint correlations between QTL and phenotypic traits for specific conditions, ignoring or overlooking the importance of environment or crop management can jeopardize the relevance of such assessments. Here, we focus on fruit load (a measure determining competition among fruits on one plant) and its strong modulation of QTL effects on fruit size and composition. Following an integral approach, we show which fruit traits are affected by fruit load, to which underlying processes these traits can be linked, and which processes at lower and higher integration levels are affected by fruit load (and subsequently influence fruit traits). This opinion paper (i) argues that a mechanistic framework to interpret interactions between fruit load and QTL effects is needed, (ii) pleads for consideration of the context of agronomic management when detecting QTL, (iii) makes a case for incorporating interacting factors in the experimental set-up of QTL mapping studies, and (iv) provides recommendations to improve efficiency in QTL detection and use, with particular focus on model-based marker-assisted breeding.

Plant interactions with multiple insect herbivores: from community to genes

Every plant is a member of a complex insect community that consists of tens to hundreds of species that belong to different trophic levels. The dynamics of this community are critically influenced by the plant, which mediates interactions between community members that can occur on the plant simultaneously or at different times. Herbivory results in changes in the plant's morphological or chemical phenotype that affect interactions with subsequently arriving herbivores. Changes in the plant's phenotype are mediated by molecular processes such as phytohormonal signaling networks and transcriptomic rearrangements that are initiated by oral secretions of the herbivore. Processes at different levels of biological complexity occur at timescales ranging from minutes to years. In this review, we address plant-mediated interactions with multiple species of the associated insect community and their effects on community dynamics, and link these to the mechanistic effects that multiple attacks have on plant phenotypes.

Plant bioassay to assess the effects of allelochemicals on the metabolome of the target species Aegilops geniculata by an NMR-based approach

A metabolomic-based approach for the study of allelopathic interactions in the Mediterranean area is proposed using Aegilops geniculata Roth (Poaceae), a Mediterranean herbaceous plant, as test species. Its metabolome has been elucidated by 1D and 2D NMR experiments. Hydroponic plant cultures of A. geniculata were treated with specific compounds of known allelopathic potential: catechol, coumarin, p-coumaric acid, p-hydroxybenzoic acid, ferulic acid and juglone. The metabolic variations due to the presence of allelochemicals have been analyzed and measured. All of the compounds showed the strongest effects at the highest concentration, with coumarin and juglone as the most active compounds, causing an increase of several metabolites. The metabolome changes in test plants confirmed the allelochemicals' reported modes of action. The results demonstrated that the proposed method is a promising tool. It can be applied to plant extracts, making it possible to evidence the metabolites responsible for the activity, as well as their mechanisms of action.

Predictive modelling of complex agronomic and biological systems

Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real-world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead.

Integrated -omics: a powerful approach to understanding the heterogeneous lignification of fibre crops

Lignin and cellulose represent the two main components of plant secondary walls and the most abundant polymers on Earth. Quantitatively one of the principal products of the phenylpropanoid pathway, lignin confers high mechanical strength and hydrophobicity to plant walls, thus enabling erect growth and high-pressure water transport in the vessels. Lignin is characterized by a high natural heterogeneity in its composition and abundance in plant secondary cell walls, even in the different tissues of the same plant. A typical example is the stem of fibre crops, which shows a lignified core enveloped by a cellulosic, lignin-poor cortex. Despite the great value of fibre crops for humanity, however, still little is known on the mechanisms controlling their cell wall biogenesis, and particularly, what regulates their spatially-defined lignification pattern. Given the chemical complexity and the heterogeneous composition of fibre crops' secondary walls, only the use of multidisciplinary approaches can convey an integrated picture and provide exhaustive information covering different levels of biological complexity. The present review highlights the importance of combining high throughput -omics approaches to get a complete understanding of the factors regulating the lignification heterogeneity typical of fibre crops.

Systems biology and "omics" tools: a cooperation for next-generation mycorrhizal studies

Omics tools constitute a powerful means of describing the complexity of plants and soil-borne microorganisms. Next generation sequencing technologies, coupled with emerging systems biology approaches, seem promising to represent a new strategy in the study of plant-microbe interactions. Arbuscular mycorrhizal fungi (AMF) are ubiquitous symbionts of plant roots, that provide their host with many benefits. However, as obligate biotrophs, AMF show a genetic, cellular and physiological complexity that makes the study of their biology as well as their effective agronomical exploitation rather difficult. Here, we speculate that the increasing availability of omics data on mycorrhiza and of computational tools that allow systems biology approaches represents a step forward in the understanding of arbuscular mycorrhizal symbiosis. Furthermore, the application of this study-perspective to agriculturally relevant model plants, such as tomato and rice, will lead to a better in-field exploitation of this beneficial symbiosis in the frame of low-input agriculture.

Understanding plant cold hardiness: an opinion

How plants adapt to freezing temperatures and acclimate to survive the formation of ice within their tissues has been a subject of study for botanists and plant scientists since the latter part of the 19th century. In recent years, there has been an explosion of information on this topic and molecular biology has provided new and exciting opportunities to better understand the genes involved in cold adaptation, freezing response and environmental stress in general. Despite an exponential increase in our understanding of freezing tolerance, understanding cold hardiness in a manner that allows one to actually improve this trait in economically important crops has proved to be an elusive goal. This is partly because of the growing recognition of the complexity of cold adaptation. The ability of plants to adapt to and survive freezing temperatures has many facets, which are often species specific, and are the result of the response to many environmental cues, rather than just low temperature. This is perhaps underappreciated in the design of many controlled environment experiments resulting in data that reflects the response to the experimental conditions but may not reflect actual mechanisms of cold hardiness in the field. The information and opinions presented in this report are an attempt to illustrate the many facets of cold hardiness, emphasize the importance of context in conducting cold hardiness research, and pose, in our view, a few of the critical questions that still need to be addressed.

Building simulation models of developing plant organs using VirtualLeaf

Cell-based computational modeling and simulation are becoming invaluable tools in analyzing plant -development. In a cell-based simulation model, the inputs are behaviors and dynamics of individual cells and the rules describe responses to signals from adjacent cells. The outputs are the growing tissues, shapes and cell-differentiation patterns that emerge from the local, chemical and biomechanical cell-cell interactions. Here, we present a step-by-step, practical tutorial for building cell-based simulations of plant development with VirtualLeaf, a freely available, open-source software framework for modeling plant development. We show how to build a model of a growing tissue, a reaction-diffusion system on a growing domain, and an auxin transport model. The aim of VirtualLeaf is to make computational modeling better accessible to experimental plant biologists with relatively little computational background.

Towards multiscale plant models: integrating cellular networks

One of the ambitions of 'crop systems biology' is to combine information from molecular biology with a broader view of plant development and growth. In the context of modeling, this calls for a multiscale perspective that focuses on the interplay between cellular and macroscopic studies. With this in mind, in this review we aim to draw attention to a panel of approaches that were developed in the context of systems biology and are used for analyzing and describing the behavior of cellular networks. Ultimately, insights obtained from these methods can be exploited to refine the description of plant processes, leading to integrated plant-cellular models.

Towards multiscale plant models: integrating cellular networks

One of the ambitions of 'crop systems biology' is to combine information from molecular biology with a broader view of plant development and growth. In the context of modeling, this calls for a multiscale perspective that focuses on the interplay between cellular and macroscopic studies. With this in mind, in this review we aim to draw attention to a panel of approaches that were developed in the context of systems biology and are used for analyzing and describing the behavior of cellular networks. Ultimately, insights obtained from these methods can be exploited to refine the description of plant processes, leading to integrated plant-cellular models.

Network inference via adaptive optimal design

Background

Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design.

Findings

The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP) as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure.

Conclusion

We conclude that a careful experiment design very often (but not always) pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.

Association mapping of plant resistance to insects

Association mapping is rapidly becoming an important method to explore the genetic architecture of complex traits in plants and offers unique opportunities for studying resistance to insect herbivores. Recent studies indicate that there is a trade-off between resistance against generalist and specialist insects. Most studies, however, use a targeted approach that will easily miss important components of insect resistance. Genome-wide association mapping provides a comprehensive approach to explore the whole array of plant defense mechanisms in the context of the generalist-specialist paradigm. As association mapping involves the screening of large numbers of plant lines, specific and accurate high-throughput phenotyping (HTP) methods are needed. Here, we discuss the prospects of association mapping for insect resistance and HTP requirements.

A holistic approach to marine eco-systems biology

The structure, robustness, and dynamics of ocean plankton ecosystems remain poorly understood due to sampling, analysis, and computational limitations. The Tara Oceans consortium organizes expeditions to help fill this gap at the global level.

Understanding shoot branching by modelling form and function

Shoot branching plays a pivotal role in the development of the aboveground plant structure. Therefore, to understand branching in relation to the environment, it is not only necessary to integrate the knowledge on mechanisms that regulate branching at multiple levels of biological organisation, but also to include plant structure explicitly. To this end, we propose the application of an established methodology called functional-structural plant modelling.

Water deficit and growth. Co-ordinating processes without an orchestrator?

Water deficit affects plant growth via reduced carbon accumulation, cell number and tissue expansion. We review the ways in which these processes are co-ordinated. Tissue expansion and its sensitivity to water deficit may be the most crucial process, involving tight co-ordination between the mechanisms which govern cell wall mechanical properties and plant hydraulics. The analyses of sensitivities, time constants and genetic correlations suggest that tissue expansion is loosely co-ordinated with cell division and carbon accumulation which may have limited direct effects on growth under water deficit. We therefore argue for essentially uncoupled mechanisms with feedbacks between them, rather than for a co-ordinated re-programming of all processes. Consequences on plant modelling and plant breeding in dry environment are discussed.

Natural genetic variation in plant photosynthesis

Natural genetic variation in plant photosynthesis is a largely unexplored and as a result an underused genetic resource for crop improvement. Numerous studies show genetic variation in photosynthetic traits in both crop and wild species, and there is an increasingly detailed knowledge base concerning the interaction of photosynthetic phenotypes with their environment. The genetic factors that cause this variation remain largely unknown. Investigations into natural genetic variation in photosynthesis will provide insights into the genetic regulation of this complex trait. Such insights can be used to understand evolutionary processes that affect primary production, allow greater understanding of the genetic regulation of photosynthesis and ultimately increase the productivity of our crops.